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      Objective, Subjective, and Accurate Reporting of Social Media Use: No Evidence That Daily Social Media Use Correlates With Personality Traits, Motivational States, or Well-Being


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          There is a lively debate on the effects of social media use, shaped by self-reported measurements of social media use. However, self-reports have been shown to suffer from low accuracy compared to logged measures of social media use. Even though it is unclear how problematic that measurement error is for our inferences, many scholars call for the exclusive use of “objective” measures. But if measurement error is not systematic, self-reports will still be informative. In contrast, if there is systematic error, associations between social media use and other variables, including well-being, are likely biased. Here, we report an exploratory 5 day experience sampling study among 96 participants (435 observations) to understand factors that could relate to low accuracy. First, we asked what stable individual differences are related to low accuracy. Second, we explored what daily states relate to accuracy. Third, we explored whether accuracy relates to well-being. Although we did find evidence for a systematic tendency to overestimate social media use, neither individual differences nor daily states were related to that tendency. Accuracy was also unrelated to well-being. Our results suggest that blindly calling for objective measures foregoes a responsibility to understand measurement error in social media use first.

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          brms: An R Package for Bayesian Multilevel Models Using Stan

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            Random effects structure for confirmatory hypothesis testing: Keep it maximal.

            Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.
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              From alpha to omega: a practical solution to the pervasive problem of internal consistency estimation.

              Coefficient alpha is the most popular measure of reliability (and certainly of internal consistency reliability) reported in psychological research. This is noteworthy given the numerous deficiencies of coefficient alpha documented in the psychometric literature. This mismatch between theory and practice appears to arise partly because users of psychological scales are unfamiliar with the psychometric literature on coefficient alpha and partly because alternatives to alpha are not widely known. We present a brief review of the psychometric literature on coefficient alpha, followed by a practical alternative in the form of coefficient omega. To facilitate the shift from alpha to omega, we also present a brief guide to the calculation of point and interval estimates of omega using a free, open source software environment. © 2013 The British Psychological Society.

                Author and article information

                Technology, Mind, and Behavior
                American Psychological Association
                August 12, 2021
                : 2
                : 2
                [1]Oxford Internet Institute, University of Oxford
                [2]Department of Psychology, Durham University
                [3]School of Psychology and Clinical Language Sciences, University of Reading
                Author notes
                Action Editor: Danielle S. McNamara was the action editor for this article.
                Grants from the Huo Family Foundation and Economic and Social Research Council (ES/T008709/1) supported Niklas Johannes and Andrew K. Przybylski. A grant from the European Research Council (851890; SOAR) supported Netta Weinstein. The funders had no role in study design, data analysis, decision to publish, or preparation of the article.
                Conflicts of Interest: The authors declare no conflicts of interest.
                Data Availability: The authors share all materials, data, and code on the Open Science Framework project for this article at https://doi.org/10.17605/OSF.IO/7BYVT ( Johannes, Nguyen, et al., 2021). The source code has been published on https://github.com/digital-wellbeing/smartphone-use. They documented all steps from raw data processing to final analysis on https://digital-wellbeing.github.io/smartphone-use/.
                Author Contributions: Conceptualization: Thuy-vy Nguyen, Netta Weinstein, and Andrew K. Przybylski. Data Curation: Niklas Johannes and Thuy-vy Nguyen. Formal Analysis: Niklas Johannes. Funding Acquisition: Netta Weinstein and Andrew K. Przybylski. Investigation: Thuy-vy Nguyen. Methodology: Niklas Johannes, Thuy-vy Nguyen, Netta Weinstein, and Andrew K. Przybylski. Project Administration: Thuy-vy Nguyen, Netta Weinstein, and Andrew K. Przybylski. Resources: Thuy-vy Nguyen, Netta Weinstein, and Andrew K. Przybylski. Software: Niklas Johannes. Supervision: Andrew K. Przybylski. Validation: Niklas Johannes. Visualization: Niklas Johannes. Writing—Original Draft Preparation: Niklas Johannes. Writing—Review and Editing: Niklas Johannes, Thuy-vy Nguyen, Netta Weinstein, and Andrew K. Przybylski.
                Disclaimer: Interactive content is included in the online version of this article.
                Open Science Disclosures:

                The data are available at https://doi.org/10.17605/OSF.IO/7BYVT

                The experiment materials are available at https://doi.org/10.17605/OSF.IO/7BYVT

                [*] Niklas Johannes, Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, United Kingdom niklas.johannes@oii.ox.ac.uk
                Author information
                © 2021 The Author(s)

                This article has been published under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright for this article is retained by the author(s). Author(s) grant(s) the American Psychological Association the exclusive right to publish the article and identify itself as the original publisher.

                Self URI (journal-page): https://tmb.apaopen.org/

                Education,Psychology,Vocational technology,Engineering,Clinical Psychology & Psychiatry
                personality,motivation,social media use,well-being,measurement


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